The Digital Twin: Modular Model-Based Approach to Personalized Medicine

Gaebel, Jan; Keller, J.M.; Schneider, Daniel J.; Lindenmeyer, Adrian; Neumuth, Thomas; Franke, Stefan

doi:10.1515/cdbme-2021-2057

Cited by 8 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…149 articles. Articles on this topic cover digital twins as decision-support instruments [45], digital twins of humans, including ethical aspects [46] and perspectives of digital-twin technology in medicine [47]. 7.…”

mentioning

confidence: 99%

Digital Twins: A Systematic Literature Review Based on Data Analysis and Topic Modeling

Kukushkin¹,

Ryabov²,

Боровков³

2022

Data

View full text Add to dashboard Cite

The digital twin has recently become a popular topic in research related to manufacturing, such as Industry 4.0, the industrial internet of things, and cyber-physical systems. In addition, digital twins are the focus of several research areas: construction, urban management, digital transformation of the economy, medicine, virtual reality, software testing, and others. The concept is not yet fully defined, its scope seems unlimited, and the topic is relatively new; all this can present a barrier to research. The main goal of this paper is to develop a proper methodology for visualizing the digital-twin science landscape using modern bibliometric tools, text-mining and topic-modeling, based on machine learning models—Latent Dirichlet Allocation (LDA) and BERTopic (Bidirectional Encoder Representations from Transformers). The scope of the study includes 8693 publications on the topic selected from the Scopus database, published between January 1993 and September 2022. Keyword co-occurrence analysis and topic-modeling indicate that studies on digital twins are still in the early stage of development. At the same time, the core of the topic is growing, and some topic clusters are emerging. More than 100 topics can be identified; the most popular and fastest-growing topic is ‘digital twins of industrial robots, production lines and objects.’ Further efforts are needed to verify the proposed methodology, which can be achieved by analyzing other research fields.

show abstract

mentioning

confidence: 99%

Digital Twins: A Systematic Literature Review Based on Data Analysis and Topic Modeling

Kukushkin¹,

Ryabov²,

Боровков³

2022

Data

View full text Add to dashboard Cite

show abstract

“…However, besides the advantages targeting modularization and transparency, the procedure also introduces significant overhead as a single change to the overarching knowledge base triggers a modification of several components within the architecture. In addition, a DT system requires high levels of maintenance not required in a highly specialized CDSS system ( 49 ). Thus, a higher potential for errors is introduced.…”

Section: Discussionmentioning

confidence: 99%

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment

Grieb,

Schmierer,

Kim

et al. 2023

Front. Digit. Health

Self Cite

View full text Add to dashboard Cite

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

show abstract

“…Each application involved in the clinical processes relies on a common infrastructure (the DMT). The implementation of the DMT follows the concept proposed in [5].…”

Section: Methodsmentioning

confidence: 99%

Using Digital Twins to Support Multiple Stages of the Patient Journey

et al. 2023

View full text Add to dashboard Cite

New possibilities in personalized medicine need to be complemented by clinical decision support systems as well as context-specific applications to be used in clinical routine. We aim to implement a shared technical backend for a large variety of applications in personalized head-and-neck cancer treatment. The infrastructure is conceptualized as a multi-purpose digital twin for cancer treatment. A set of prototypes of clinical applications demonstrates the feasibility of using digital twins to support multiple stages of the patient journey.

show abstract

The Digital Twin: Modular Model-Based Approach to Personalized Medicine

Cited by 8 publications

References 0 publications

Digital Twins: A Systematic Literature Review Based on Data Analysis and Topic Modeling

Digital Twins: A Systematic Literature Review Based on Data Analysis and Topic Modeling

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment

Using Digital Twins to Support Multiple Stages of the Patient Journey

Contact Info

Product

Resources

About